Based on the aerodynamic theory and CFD method,the single/multi-objective aerodynamic optimization design of a truck deflector is *** the process,two approaches have been developed separately to improve its performanc...
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Based on the aerodynamic theory and CFD method,the single/multi-objective aerodynamic optimization design of a truck deflector is *** the process,two approaches have been developed separately to improve its performance:(A) a suitable cut transversely on the back yard of the circular deflector;(B) deflector reshaping using *** single-objectiveoptimization,truck speed is fixed as the precondition,drag is set to be the ***,the multi-objectiveoptimization is arranged by Hybrid Genetic Algorithm (HGA),including drag and aerodynamic *** research indicates that both approaches can improve the local flow status,and provide a better characteristics in reducing drag(4.50%-6.23%and 3.16%-4.03%respectively) and acoustics,which is valuable both in academic research practical application.
A novel multi-objective optimizer has been introduced based on the learning automata (LA) and utilized to develop a multi-objective classifier (named MLAclassifier). The proposed classification method is able to appro...
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We consider learning formulations with non-convex objective functions that often occur in practical applications. There are two approaches to this problem: Heuristic methods such as gradient descent that only find a l...
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We consider learning formulations with non-convex objective functions that often occur in practical applications. There are two approaches to this problem: Heuristic methods such as gradient descent that only find a local minimum. A drawback of this approach is the lack of theoretical guarantee showing that the local minimum gives a good solution. Convex relaxation such as L1-regularization that solves the problem under some conditions. However it often leads to a sub-optimal solution in reality. This paper tries to remedy the above gap between theory and practice. In particular, we present a multi-stage convex relaxation scheme for solving problems with non-convex objective functions. For learning formulations with sparse regularization, we analyze the behavior of a specific multistage relaxation scheme. Under appropriate conditions, we show that the local solution obtained by this procedure is superior to the global solution of the standard L-1 convex relaxation for learning sparse targets.
In many practical situations, we need to optimize several objectives under the positivity constraints. For example, in meteorological and environmental studies, it is important to collect various types of data, such a...
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In many practical situations, we need to optimize several objectives under the positivity constraints. For example, in meteorological and environmental studies, it is important to collect various types of data, such as temperature and wind speed and direction, from weather stations. For maintenance purposes, it is convenient to place instruments that collect different weather data on the same weather station. Thus, we need to find the “best” location for a weather station. The “best” means, for example, that the external influences, such as flux of cars passing on nearby road, have a minimal impact on the measurement results. There are several such criteria, so we face a multi-objectiveoptimization problem. In this paper, we show that traditional approaches for solving such problems - such as the weighted sum approach - are not fully adequate for solving our problem. We show that fuzzy heuristics lead to a more adequate approach - of using a generalized form of Nash bargaining solution. We then prove that under reasonable assumptions of scale-invariance, the generalized Nash bargaining solution is the only adequate solution for the general problem of multi-objectiveoptimization under positivity constraints - and, in particular, for the problem of selecting an optimal location for a weather station.
The main focus of this study is on the development of an efficient and effective hull surface modification technique for the CFD-based hull form *** approaches are *** is based on the radial basis function interpolati...
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The main focus of this study is on the development of an efficient and effective hull surface modification technique for the CFD-based hull form *** approaches are *** is based on the radial basis function interpolation,and the other the sectional area curve of the *** local and global modifications of hull forms can be achieved by combining these two *** hull surface modification technique developed in this study is used to vary the hull forms during the optimization process,in which the objective functions associated with the resistance is evaluated by a practical design-oriented CFD tool(SSF),and a multi-objective genetic algorithm is adopted to allow for multi-design *** the purpose of illustration,the KRISO container ship(KCS) is taken as an initial hull to be optimized for reduced drag at given design *** results obtained in this study have shown that the present hull surface modification technique can produce smooth hull forms with reduced drag effectively and efficiently in the CFD-based hull form optimization.
Most research on Strip Packing Problems is focused on the single-objective formulation of the problem. However, in this work we deal with a more general and practical variant of the problem, which not only seeks to op...
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Most research on Strip Packing Problems is focused on the single-objective formulation of the problem. However, in this work we deal with a more general and practical variant of the problem, which not only seeks to optimize the usage of the raw material, but also the production process. For the problem solution, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behavior when affording multi-objective real-world problems. For an initial implementation, we proposed a solution codification which is based on a complete representation of the pattern layouts. Such an approach was promising but wasn't suitable to afford large instances. For this reason, we have focused on the design of a codification which can be much more competitive when compared to some tailor-made methods. In this sense, we present a hyperheuristic-based codification as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another. Results demonstrate the advantage of using multi-objectiveapproaches, hyperheuristic-based representations, and of course, the importance on the selection of appropriate solution codifications.
This paper considers the design and optimization of thermal systems on the basis of the mathematical and numerical modeling of the system. Many complexities are often encountered in practical thermal processes and sys...
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This paper considers the design and optimization of thermal systems on the basis of the mathematical and numerical modeling of the system. Many complexities are often encountered in practical thermal processes and systems, making the modeling challenging and involved. These include property variations, complicated regions, combined transport mechanisms, chemical reactions, and intricate boundary conditions. The paper briefly presents approaches that may be used to accurately simulate these systems. Validation of the numerical model is a particularly critical aspect and is discussed. It is important to couple the modeling with the system performance, design, control and optimization. This aspect, which has often been ignored in the literature, is considered in this paper. Design of thermal systems based on concurrent simulation and experimentation is also discussed in terms of dynamic data-driven optimization methods. optimization of the system and of the operating conditions is needed to minimize costs and improve product quality and system performance. Different optimization strategies that are currently used for thermal systems are outlined, focusing on new and emerging strategies. Of particular interest is multi-objectiveoptimization, since most thermal systems involve several important objective functions, such as heat transfer rate and pressure in electronic cooling systems. A few practical thermal systems are considered in greater detail to illustrate these approaches and to present typical simulation, design and optimization results. (C) 2008 Elsevier Ltd. All rights reserved.
The proceedings contain 18 papers. The topics discussed include: a new adaptive algorithm for convex quadratic multicriteria optimization;a new approach on many objective diversity measurement;an adaptive scheme to ge...
The proceedings contain 18 papers. The topics discussed include: a new adaptive algorithm for convex quadratic multicriteria optimization;a new approach on many objective diversity measurement;an adaptive scheme to generate the Pareto front based on the Epsilon-constraint method;application issues for multiobjective evolutionary algorithms;approximation and visualization of pareto frontier in the framework of classical approach to multi-objectiveoptimization;current status of the EMOO repository, including current and future research trends;effects of crossover operations on the performance of EMO algorithms;and multi-criteria ranking of a finite set of alternatives using ordinal regression and additive utility functions - a new UTA-GMS method.
In this paper we extend the work, where authors have proposed a evolutionary multi-objective approach to Rapid Prototyping (RP), to decipher optimal build orientation strategies by systematic post-analysis of optimal ...
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In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence desi...
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ISBN:
(纸本)9783642024801
In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an essential problem and one of the most practical and important research topics. Basically, the DNA sequence design problem is a multi-objective problem, and it can be evaluated using four objective functions, namely, H-measure, similarity, continuity, and hairpin. There are several ways to solve a multi-objective problem, such as value function method, weighted sum method, and using evolutionary algorithms. However, in this paper, common method has been used, namely weighted sum method to convert DNA sequence design problem into single objective problem. Binary particle swarm optimization (BinPSO) is proposed to minimize the objective in the problem, subjected to two constraints: melting temperature and GC(content), Based on experiments and researches done, 20 particles are used in the implementation of the optimization process, where the average values and the standard deviation for 100 runs are shown along with comparison to other existing methods. The results obtained verified that BinPSO can suitably solve DNA sequence design problem using the proposed method and model, comparatively better than other approaches.
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